System to manage economics and operational dynamics of IT systems and infrastructure in a multi-vendor service environment

ABSTRACT

A method is provided to manage economics and operational dynamics of various information technology (IT) systems. A computer collects data indicative of operation of a plurality of hardware components and collects data indicative of operation of a plurality of software components. The computer creates a first qualitative value representing a hardware status of the plurality of the hardware components and a second qualitative value representing a software status of the plurality of the software components. The first and second qualitative values are displayed in graphical form for evaluation by a system operator, and the computer computes a probability of life expectancy for the plurality of hardware components and the plurality of software components based on said first and second qualitative values and utilizing cognitive and artificial intelligence based calculations to determine the probability.

BACKGROUND OF THE INVENTION Technical Field

The present invention is a system to achieve to manage economics andoperational dynamics of various information technology (IT) systems andinfrastructure in a multi-vendor service environment.

Discussion of the Related Art

Currently IT infrastructure is heterogeneous with various kinds ofcompute devices, network devices and portable/hand held devices ofvarious form, fit and function with various ownership levels includingbut not limited to company owned, outsourced and “Bring Your Own Device”devices. While each device in the infrastructure is capable of runningbeyond the supported end of life (EOL), the challenge is to efficientlyrun the system till the real end of life. When an error occurs or a partof a hardware fails, it is easy to throw the problem on to end of lifeof system, whereas analytic based root cause analysis can estimate andextend residual life with appropriate software changes, execution plans,change in hardware components etc. based on system performance includingsoftware and hardware.

Existing approaches to solving address this problem are concerned mostlywith the data center infrastructure. The methods focus purely onsoftware based failures as in cloud infrastructure, or hardwarereplacements at the data center endpoints. These systems and methods arelimited from a user perspective because quality of service is impactedirrespective of where the device exists. The existing methods look atonly part of the system environment.

SUMMARY

The present invention is a system to achieve to manage economics andoperational dynamics of various IT systems and infrastructure in amulti-vendor service environment. A method is provided to manageeconomics and operational dynamics of various information technology(IT) systems. A computer collects data indicative of operation of aplurality of hardware components and collects data indicative ofoperation of a plurality of software components. The computer creates afirst qualitative value representing a hardware status of the pluralityof the hardware components and a second qualitative value representing asoftware status of the plurality of the software components. The firstand second qualitative values are displayed in graphical form forevaluation by a system operator, and the computer computes a probabilityof life expectancy for the plurality of hardware components and theplurality of software components based on said first and secondqualitative values and utilizing cognitive and artificial intelligencebased calculations to determine the probability.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of severalembodiments of the present invention will be more apparent from thefollowing more particular description thereof, presented in conjunctionwith the following drawings. Corresponding reference characters indicatecorresponding components throughout the several views of the drawings.Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of various embodiments of the present invention.Also, common but well-understood elements that are useful or necessaryin a commercially feasible embodiment are often not depicted in order tofacilitate a less obstructed view of these various embodiments of thepresent invention.

FIG. 1 illustrates a system for determining hardware life-expectancybased on historical data collection according to an embodiment of thepresent invention.

FIG. 2 illustrates a server and a computing device coupled through anetwork in a system for determining hardware and softwarelife-expectancy according to an embodiment of the present invention.

FIG. 3 is a flowchart showing an overview of the proposed system toachieve the system to manage economics and operational dynamics ofvarious IT systems and infrastructure in a multi-vendor serviceenvironment according to an embodiment of the present invention.

FIG. 4 is a flowchart showing steps to accomplish an embodiment of thepresent invention.

FIG. 4A illustrates a three dimensional computation of the probabilityof stuck key according to an embodiment of the present invention.

FIG. 4B illustrates an example of the odometer system provided by thisinvention whereby individual and cumulative performance rates ofhardware and software components are displayed visually for an operatoraccording to an embodiment of the present invention.

FIG. 4C illustrates an example of a sensor and data collection systemfor a keyboard in accordance with an embodiment of the presentinvention.

FIG. 5 illustrates an alternate embodiment of the present invention withthe components illustrated in FIG. 3 rearranged to provide the mosteconomical output suitable for use by the system administrator.

FIG. 6 illustrates a further alternate embodiment of the presentinvention with the components illustrated in FIG. 3 rearranged toprovide the most economical output suitable for use by the systemadministrator.

FIG. 7 illustrates the schematic representation of the processing ofdata as processed various techniques described above to produce asystem/server end of life calculation.

FIG. 8a illustrates a data table for different sub-components utilizedto calculate an expect risk score including variables, operationalcycles and expected life as utilized by the expert system according toan embodiment of the present invention.

FIG. 8b illustrates a chart of operational cycle versus expected lifebased on the data table of FIG. 8a as utilized by the expert systemaccording to an embodiment of the present invention.

FIG. 8c illustrates a chart of failure probability based on the data ofFIGS. 8a and 8b according to an embodiment of the present invention.

FIG. 8d illustrates a chart of the expected risk score as determined bythe expert system with the chart showing probability of particularfailure mode (e.g. stuck mode) along the x-axis for a keyboard and thesub-component level of expected risk score along the y-axis according toan embodiment of the present invention.

FIG. 9a illustrates a data table for different sub-components utilizedto calculate an ecosystem level expect risk score including variables,operational cycles and expected life as utilized by the expert systemaccording to an embodiment of the present invention.

FIG. 9b illustrates a chart of operational cycle versus expected lifebased on the data table of FIG. 9a as utilized by the expert systemaccording to an embodiment of the present invention.

FIG. 9c illustrates a chart of failure probability based on the data ofFIGS. 9a and 9b according to an embodiment of the present invention.

FIG. 9d illustrates a chart of the component 901, system 902 andecosystem 903 expected risk score as determined by the expert systemaccording to an embodiment of the present invention.

FIG. 10 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The following description is not to be taken in a limiting sense, but ismade merely for the purpose of describing the general principles ofexemplary embodiments. The scope of the invention should be determinedwith reference to the claims.

Reference throughout this specification to “one embodiment,” “anembodiment,” or similar language means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention. Thus,appearances of the phrases “in one embodiment,” “in an embodiment,” andsimilar language throughout this specification may, but do notnecessarily, all refer to the same embodiment.

Furthermore, the described features, structures, or characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. In the following description, numerous specific details areprovided, such as examples of programming, software modules, userselections, network transactions, database queries, database structures,hardware modules, hardware circuits, hardware chips, etc., to provide athorough understanding of embodiments of the invention. One skilled inthe relevant art will recognize, however, that the invention can bepracticed without one or more of the specific details, or with othermethods, components, materials, and so forth. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring aspects of the invention.

Real-time occurrences as referenced herein are those that aresubstantially current within the context of human perception andreaction.

In a computational/storage Information Technology (IT) infrastructure,especially in one composed of more than a single unit, for example, butnot limited to, clusters of computers or cloud infrastructures, hardwarecomponents and software modules do not age uniformly. This is a resultof many factors, including the fact that software and hardwarecomponents in a system do not have equal operational lifetime. Forexample, random-access memory (RAM) is expected to live longer than ahard disk. And a solid state drive (SSD) family hard disk is expected tolive longer than a conventional (mechanical) hard disk. Another factorcontributing to aging heterogeneity is the operational temperature ofhardware components. Accordingly, operational temperature influences thelife of all hardware components to various degrees, from shortening topotentially terminating them if they stay long periods in a danger zone.Furthermore, in some embodiments different hardware components aresubjected to different operational temperatures. A need exists for asystem and a method for accurately predicting a lifetime of individualhardware components such that preventive action may be taken in advanceof system failure. A need also exists a system and a method to prolongthe lifetime of a system of hardware components by accurately evaluatingthe degradation of each hardware component.

Environmental conditions such as outside temperature, ventilation,humidity and dust influence operational temperature, which in turninfluences lifetime. Accordingly, environmental conditions may notequivalently impact all hardware components when environmentalconditions are not uniform across the system. Another factor in theheterogeneous lifetime of hardware components is load, which is highlyvariable between hardware components. Load in a hardware componentinfluences operational voltage and temperature, thus impacting thelifetime of the hardware component. In a multi-unit environment, such asa computer cluster, and especially in a cloud-like setup, aging of unitsis usually highly disproportionate. Some hardware components may stayidle for long periods of time and as such accumulate limiteddegradation, whereas other hardware components reach their end of lifeearlier than expected when used intensively.

Accordingly, a hardware component status is determined based on criticallevel values provided by a manufacturer combined with cumulative values,and adding a time dimension by considering a historical record ofhardware component parameter values. Critical level values may bedetermined under test conditions by the manufacturer. In someembodiments, the critical level is similar to or substantially equal toa factory end of life (EOL) value. Cumulative values may be obtainedfrom a historical record of parameter values stored within dedicatedstorage devices residing inside the equipment itself, or in a networkserver accessible to the equipment administrator. Accordingly, a systemand a method as disclosed herein determine in timely fashion the lifeexpectancy of equipment using a historical record of hardware componentparameter values. Moreover, the determination of life expectancy isaccurate because the method accounts for variations in the operationalconditions of the equipment.

Embodiments disclosed herein quantify the degradation status of softwareand hardware components, making the result available for observation toboth human and computation agents such as an Application ProgrammingInterface (API). Determination of this life expectancy of computersoftware and hardware is accomplished by a compilation of statisticaland factory information combined with accurate historical data about theactual operating parameters of specific software and hardwarecomponents. The historical data is acquired through specialized probeagents and stored in a centralized manner such that analysis andprediction can be formulated. Once formulated, appropriate predictionsand alerts regarding the potential lifetime left in the software andhardware components are generated. Further, in some embodiments theprobe agents use the prediction analysis to take preventive action aheadof upcoming failures in certain modules or parts of the system. Suchpreventive actions include increasing a sampling rate or generatingalerts.

Some embodiments of the present disclosure quantify a degradation statusof individual software and hardware components in a computer system.Accordingly, some embodiments include monitoring and recording parametervalues of the hardware component across at least a portion of thehardware component lifetime. In some embodiments, the monitoring iscontinuous and spans the entire lifetime of the hardware component. Morespecifically, some embodiments combine data aggregates directly obtainedfrom the hardware component with statistical information availablethrough different sources, for each hardware component. Furthermore,some embodiments provide the results to probe agents (human orcomputational) for inspection and response, if desired. Some embodimentsfurther provide estimates, predictions and alerts regarding theremaining lifetime in the hardware component, enabling observing agentsto prepare for possible failures in certain parts of the system. In thatregard, some embodiments further provide a method to prolong the usablelife of the computer system by identifying problems affecting the lifeexpectancy of each of the hardware components in the computer systembefore they fail. In some embodiments, a record of the operationparameters throughout the lifetime of the equipment is maintained withthe aid of a low footprint probe agent that resides on each individualoperating system. A central computing system residing on a server andhaving access to the record of the operation parameters computescomprehensive degradation values for the hardware components. Thecentral computing system also generates reports and alerts long beforethe equipment fails or is about to fail, thus leaving ample time toprepare for hardware migration, if desired. The hardware maintenancemodel is prophylactic in that it provides corrective action prior tooccurrence of a loss event. Having a record of the operation parametersthrough at least a portion of the lifetime of the hardware componentsenables methods and systems as disclosed herein to formulate an accurateprediction regarding the degradation status of the hardware components.

FIG. 1 illustrates a system 100 for determining hardware life expectancybased on historical data collection, according to an embodiment of thepresent invention. System 100 includes a server 110 and client devices120-1 through 120-5 coupled over a network 150. Each of client devices120-1 through 120-5 (collectively referred to hereinafter as clientdevices 120) is configured to include a plurality of hardwarecomponents. Client devices 120 can be, for example, a tablet computer, adesktop computer, a server computer, a data storage system, or any otherdevice having appropriate processor, memory, and communicationscapabilities. Server 110 can be any device having an appropriateprocessor, memory, and communications capability for hosting informationcontent for display. The network 150 can include, for example, any oneor more of a TCP/IP network, a personal area network (PAN), a local areanetwork (LAN), a campus area network (CAN), a metropolitan area network(MAN), a wide area network (WAN), a broadband network (BBN), theInternet, and the like. Further, network 150 can include, but is notlimited to, any one or more of the following network topologies,including a bus network, a star network, a ring network, a mesh network,a star-bus network, tree or hierarchical network, and the like.

FIG. 2 illustrates a server 110 and a computing device 220 coupledthrough network 150 in a system 200 for determining software andhardware life expectancy according to an embodiment of the presentinvention. Server 110 includes a processor circuit 112, a memory circuit113, a dashboard 115, and an interconnect circuit 118. Processor circuit112 is configured to execute commands stored in memory circuit 113 sothat server 110 performs steps in methods consistent with the presentdisclosure. Interconnect circuit 118 is configured to couple server 110with network 150, so that remote users can access server 110.Accordingly, interconnect circuit 118 can include wireless circuits anddevices, such as Radio-Frequency (RF) antennas, transmitters, receivers,and transceivers. In some embodiments, interconnect circuit 118 includesan optical fiber cable, or a wire cable, configured to transmit andreceive signals to and from network 150. Memory circuit 113 can alsostore data related to client device 220 in a database 114. For example,database 114 can include historical operation data from at least one ofthe plurality of hardware components. Server 110 includes a dashboard115 to provide a graphic interface with a user for displayinginformation stored in database 114 and to receive input from the user.

Client device 220 includes a plurality of hardware components 221, aprocessor circuit 222, a memory circuit 223, and an interconnect circuit228. In some embodiments client device 220 is a redundancy system andhardware components 221 are redundancy units. In some embodiments,client device 220 is a backup system and hardware components 221 arebackup units. In that regard, the backup system may be configured todynamically store large amounts of information from a plurality ofcomputers forming a local area network (LAN). Accordingly, in someembodiments, client device 220 is configured to store large amounts ofinformation for long periods of time, and provide dynamic access, read,write, and update operations to the stored information. For example, aredundancy system or a backup system can include a server computercoupled to a local area network (LAN) to service a plurality ofcomputers in a business unit. In some embodiments hardware components221 include a battery 232, a motherboard 234, a power supply 236,keyboard 237, at least one disk drive 238, and at least one fan 239.More generally, hardware components 221 may include any hardware deviceinstalled in client device 220. In some embodiments, hardware components221 include a RAID, or a plurality of memory disks configured to backupmassive amounts of data. Moreover, in some embodiments hardwarecomponents 221 include a plurality of processor circuits 222 such ascentral processing units (CPUs). In some embodiments, hardwarecomponents 221 are configured to measure and report parameters that caninfluence their lifetime. For example, disk drive 238 may include harddisks having SMART (Self-Monitoring, Analysis and Reporting Technology)data including, but not limited to, values for rotation speed,temperature, spin up time, Input/Output (10) error rate, and total timeof operation. Likewise, CPUs in hardware components 221 can report loadvalues (in percentage), voltage and operational temperature. Motherboard234 can report operational temperatures, and voltages to server 110.Power supply 236 can report operational temperature, voltage and currentvalues. Fan 239 can report on its speed. Each of these parametersinfluence degradation (e.g., wear) as a function of momentary values andtime.

Within each individual client device 220, each elementary hardwarecomponent 221 has a unique identification (ID). The hardware componentID can include any or a combination of: component type, manufacturername, manufacturer ID, serial number, or any other availableinformation. Accordingly, the hardware component ID transcends operatingsystem re-installation. That is, the hardware component ID isindependent from a specific value used by an operating system installedin memory circuit 223 and executed by processor circuit 222. Moregenerally, processor circuit 222 is configured to execute commandsstored in memory circuit 223 so that client device 220 performs steps inmethods consistent with the present disclosure. Interconnect circuit 228is configured to couple client device 220 with network 150 and accessserver 110. Accordingly, interconnect circuit 228 can include wirelesscircuits and devices, such as Radio-Frequency (RF) antennas,transmitters, receivers, and transceivers, similarly to interconnectcircuit 118 in server 110. Interconnect circuit 228 can include aplurality of RF antennas configured to couple with network 150 via awireless communication protocol, such as cellular phone, blue-tooth,IEEE 802.11 standards (such as WiFi), or any other wirelesscommunication protocol as known in the art.

FIG. 3 is a flowchart showing an overview of the proposed system toachieve the system to manage economics and operational dynamics ofvarious IT systems and infrastructure in a multi-vendor serviceenvironment according to an embodiment of the present invention. At theoutset, historical data is collected. The historical data includeshardware historical data 312, software historical data 314, userbehavior historical data 316, and system historical data 318. Thecollected data is then delivered to a first level analytics engine 320which analyzes usage patterns, typical application installation andexecution, criticality of application software, warranty of hardwarecomponents, and compute cycles. The data analyzed by the analyticsengine at step 320 is then delivered to the system odometer 330 alongwith real-time current data 334 related to workload, response time, andintervention. Further details about the system odometer will be providedbelow.

The output of the system odometer 330 is then delivered to the expertsystem 340 where the probability of system survival for the next ‘N’years is calculated. Thus, the expert system 340 will provide anevaluation of parts and software to be replaced for the system tosurvive the next ‘N’ years, which is essentially the estimated lifeexpectancy for the hardware and software. The expert system 340 furtherprovides a cumulative asset risk evaluator (CARE) which assembleslife-expectancy data from the system odometer 330 and provides a riskevaluator.

Lastly, a Capex/Opex Optimizer 350 will forecast issues for the next ‘X’days, create a trend of resource contentions, create a sequence ofprocesses to avoid contentions (e.g., failures), create prioritizationbased on the cumulative asset risk evaluator (CARE). The optimizer 350will further provide maintenance recommendations, an optimal maintenanceschedule, and economic optimization details. It is noted that anoperating expense, operating expenditure, operational expense,operational expenditure or OPEX is an ongoing cost for running aproduct, business, or system. Its counterpart, a capital expenditure(CAPEX), is the cost of developing or providing non-consumable parts forthe product or system.

FIG. 4 is a flowchart showing steps to accomplish an embodiment of thepresent invention. At step 410, historical data of hardware at issue issourced and analyzed to provide a usage pattern and componentscriticality. The historical data includes hardware historical data,software historical data, user behavior historical data, and systemhistorical data. Step 410 is a partially generic step with additionalmodeling framework because currently none of solutions provide analyticsand the generic closed form relations may be utilized as a part of thepresent invention. The modeling framework extracts input parameters fromsub-system such as keyboard, server/system, battery, and monitor, forexample. These inputs are number of flickers, key-board response time,battery charging rate, charge hold time over time, CPU/memoryutilization, temperature, fan status, availability, latency (ping roundtrip time), packet loss, frequency of incidents, mean time to repair,key performance indicators (KPI) threshold deviations, number ofanomalies, events/log data, business criticality.

At step 420, an input matrix is created from these inputs and processedby the first level analytics engine 320 (see FIG. 3). The system willform the correlation and criticality of input matrix and state matrixwith outputs of each component, as well as the server/system.

Principal component analysis (PCA) will be applied to create dominantfeatures and regenerating input and state matrices. PCA is a statisticalprocedure that uses an orthogonal transformation to convert a set ofobservations of possibly correlated variables into a set of values oflinearly uncorrelated variables called principal components (orsometimes, principal modes of variation). The number of principalcomponents is less than or equal to the smaller of the number oforiginal variables or the number of observations. This transformation isdefined in such a way that the first principal component has the largestpossible variance (that is, accounts for as much of the variability inthe data as possible), and each succeeding component in turn has thehighest variance possible under the constraint that it is orthogonal tothe preceding components. The resulting vectors are an uncorrelatedorthogonal basis set. PCA is sensitive to the relative scaling of theoriginal variables.

At step 430, output from step 420 is input into a System Odometer 330which provides the remaining useful usage and in sync with currentinterventions, workload, and response times.

The system at step 430 creates a conglomeration of inputs of all forms,including structured as well unstructured. The conventional systems donot provide tool or means to create cloud of inputs. Thus, this is newmethod derived from combination of various data exploration techniquessuch as PCA, NLP, Rescaling, FFT, orthogonality etc. Thus, the inventionutilizes F(PCA, NLP, Rescaling, FFT, Orthogonality, vectortransformation) on data sources (structured, random, unstructured). Theoutput of step 430 is normalized input matrix.

Following the same parameters and techniques describe above, the systemstates and inputs metrics are computed.

A set-of analytics model form the relation between the set of inputs andoutputs stated; e.g., probability of malfunction and failure, number ofsimilar historical events, etc. To accomplish this step regarding theinput and state matrix, the data is passed through first a “fuzzysystem” that gives dynamic limits on system conditions. The input andstate matrix are applied on hybrid model of informationretrieval/knowledge base for unstructured data, Classification andregression trees (CART) are applied on event data. Decision Trees arecommonly used in data mining with the objective of creating a model thatpredicts the value of a target (or dependent variable) based on thevalues of several input (or independent variables). Classification andregression tree (CART) analysis recursively partitions observations inmatched data set, consisting of a categorical (for classification trees)or continuous (for regression trees) dependent (response) variable andone or more independent (explanatory) variables, into progressivelysmaller groups.

Outputs of all these models are applied as inputs to Adaptive NeuralNetwork (ANN), current interventions, actions, remedies applied are alsoused as inputs to ANN. ANN is residual useful life, Trend of resourceorientation.

Based on probable “state-transition”, past event taken place, frequencyof events, probability of event/failure/malfunction etc., the MIMO[multi input multi output] model would provide remaining or residualuseful life (RUL). This phase does not use MIMO model, MIMO is used forANN which has multiple inputs and multiple outputs. RUL is not residualuseful life based on time unlike in traditional definitions, here RUL isconsidered as life based on the feature interactions and usage patternsof system and subsystems.

“System Odometer” is a term coined after development of this invention,although the term odometer is commonly used in automotive.

Basic functions of system odometer are to calculate end of lifeparameters and display same as one might see in fuel odometers in car.For example, a hardware component such as keyboard has various failuremodes, one of failure mode is “stuck key” which can be characterized bythe time response of key input on screen, feel and pressure on thefingers which may be measured by strain gauges underneath keys, rate ofchange of key stroke per seconds, sound intensity of stroked key, etc.

The block upstream of the odometer calculates as featured above usingcombination of methods mentioned such as PCA, logistic regression,closed loop correlation, FFT etc. Then odometer block forms closed looprelation such as inputs (e.g., X) are features mentioned above andresponse or output (e.g., Y) is “stuck key.” The system odometer thenforms a probability density function of Y versus X, calculates for everyreal time data from a hardware system around keyboard, number of timesprobability has exceeded pre-calibrated threshold value for this failuremode. If continuously over time, the failure rate is above threshold formore than pre-defined intervals then that particular “key” in thekeyboard system is alerted as end of life (EOL) and intensity is showngraphically on screen through the system odometer. The same logic aboveis extended to all hardware and software components that are mentioned.

At step 440, the system odometer 330 data is delivered to the expertsystem 340 as a PDF value, which relate to the Ys, Xs computed above ofeach hardware and software components.

More specifically and with reference to FIG. 3, output from systemodometer 330 is provided as input to the “Expert System” for computingprobability of system survival for next N years, Parts to be replacedfor system to survive next N years, Estimate Life, Trend of resourcecontention, Sequence of processes to avoid contention, Prioritizationbased on Cumulative Asset Risk Evaluator (CARE), Maintenancerecommendations, Optimal Maintenance schedule, Economic Optimization,Probability of system survival for next N years, and parts to bereplaced for system to survive next N years as an estimate of lifeexpectancy.

As shown in FIG. 3, the expert system comprises combination of (8 ANN+8multivariate regression models), a ranking system, a PDF generator,Bayesian model, a CARE system and the expert system generates outputsmentioned above. More specifically, the cumulative asset risk evaluatorprovides a system whereby a risk score is calculated for each assetbased on the significant variables which influence the life of theasset. Variables like CPU/memory utilization, temperature, fan status,availability, latency (ping round trip time), packet loss, frequency ofincidents, mean time to repair, KPI threshold deviations, number ofanomalies, and events/log data are processed by the expert system 340.Business criticality is used as input for computing the risk scores. Therisk score signifies the intensity of the problem for the asset andhelps to prioritize the asset for further actions like maintenance,refresh etc. Techniques like multivariate regression/neural networks maybe applied on historic data to train and test the risk scoringanalytical models. Risk score is computed at each individual assetlevel.

Individual Asset Risk Scorea1=β_(0a1)+B_(1a1)X_(1a1)+β_(2a1)X_(2a1)+ . .. +β_(na1)X_(na1), (where β_(0a1), β_(1a1) etc. are coefficientsdetermined by techniques like regression and X_(1a1), X_(2a1) etc. arethe variables). A cumulative Asset Risk Score is calculated which is aregression of the individual asset risk scores and which provides theoverall risk to the ecosystem. The Cumulative Asset Risk Score=θ₀+θ₁Risk Score_(a1)+θ₂ Risk Score_(a2)+ . . . +θ_(n) Risk Score_(an) (whereθ₀, θ₁ are the coefficients determined by techniques like regression andRisk Score_(a1), Risk Score_(a2) etc. are the risk score for eachasset).

The above steps constitute Cumulative Asset Risk Evaluator (CARE).

Output from 440 is provided as input to Capex/Opex Optimizer 350 whichcomputes to give the final output which can be used by a systemadministrator to take further action. All of these analytics enginesprovide a final engine that includes cognitive and AI based systems andprovides following: forecast of issues for next X days, trend ofresource contentions, sequence of processes to avoid contention,maintenance recommendations, optimal maintenance schedule, and economicoptimization. These computations are accomplished in the same manners asdescribed above with respect to the analytics engine 320 and the systemodometer 330.

FIG. 4a illustrates a three dimensional computation of the probabilityof stuck key according to an embodiment of the present invention. Thethree dimensional computation illustrated by FIG. 4a of the probabilityof a stuck key (P_stuckkey) is computed using the model—EF[Environmental factors] curves which are generated with respect to timeand P_stuckkey is calibrated along acceptable EF curve. The keyboardindependent variables 470 represented along the Y-axis are the timeresponse of key input on screen (x1), feel/pressure on the fingers (x2),which may be measured by strain gauges underneath keys, the rate changeof key stroke per seconds (x3), the sound intensity of stroked key (x4),the number of maintenance (x5), and past replacement if any (x6). Thedependent variables 480 represented along the X-axis are the EOL factor,RUL factor, and “stuck key”. The environmental factors 490 representedalong the Z-axis may include geography, temperature, altitude, number ofusers, humidity, design parameters, and specified life cycle.

FIG. 4B illustrates an example of the odometer system provided by thisinvention whereby individual and cumulative performance rates ofhardware and software components are displayed visually for an operatoraccording to an embodiment of the present invention. Individual hardwarecomponents are shown with the designation Hardware X1, Hardware X2,etc., and individual software components are shown with the designationSoftware Y1, Software Y2, etc. The cumulative function of all hardwarecomponents are also illustrated by an odometer next to the similarodometer for all software components. The overall system odometer islikewise illustrated at the top of the screen of FIG. 4B.

FIG. 4C illustrates an example of a sensor and data collection systemfor a keyboard in accordance with an embodiment of the presentinvention.

FIG. 5 illustrates an alternate embodiment of the present invention withthe components illustrated in FIG. 3 rearranged to provide the mosteconomical output suitable for use by the system administrator.

FIG. 6 illustrates a further alternate embodiment of the presentinvention with the components illustrated in FIG. 3 rearranged toprovide the most economical output suitable for use by the systemadministrator.

FIG. 7 illustrates the schematic representation of the processing ofdata as processed various techniques described above to produce asystem/server end of life calculation.

FIG. 8a illustrates a data table for different sub-components utilizedto calculate an expect risk score including variables, operationalcycles and expected life as utilized by the expert system according toan embodiment of the present invention. The data is associated withsubcomponent parameter values shown in the exemplary table below:

Environ. Environ. Variable Variable Error Date Time Subcomp. Comp.Variable 1 Var. 2 variable 1 variable 2 N − 1 N code Nov. 10, 2017 13:45C4-1 C4 0.049902851 71 8 45 11 115 No error Nov. 10, 2017 14:00 C4-1 C40.240420913 55 5 30 12 95 No error Nov. 10, 2017 14:15 C4-1 C40.427968711 57 20 44 11 116 No error Nov. 10, 2017 14:30 C4-1 C40.252930392 66 9 34 14 96 No error Nov. 10, 2017 14:45 C4-1 C40.709874182 75 9 25 25 108 Err 1 Nov. 10, 2017 15:00 C4-1 C4 0.72469079772 15 27 13 111 No error Nov. 10, 2017 15:15 C4-1 C4 0.232773761 65 1434 17 95 No error Nov. 10, 2017 15:30 C4-1 C4 0.885841611 57 9 31 25 96No error Nov. 10, 2017 15:45 C4-1 C4 0.85722246 60 8 32 18 110 No errorNov. 10, 2017 16:00 C4-1 C4 0.722683637 66 8 36 10 114 No error Nov. 10,2017 16:15 C4-1 C4 0.585198872 57 16 42 7 106 Err 5 Nov. 10, 2017 16:30C4-1 C4 0.408574515 69 16 45 19 119 No error Nov. 10, 2017 16:45 C4-1 C40.632541323 69 7 32 31 111 No error Nov. 10, 2017 17:00 C4-1 C40.046076432 55 11 40 7 118 No error Nov. 10, 2017 17:15 C4-1 C40.903746877 62 14 35 31 95 Err 4 Nov. 10, 2017 17:30 C4-1 C4 0.42476109870 15 28 29 99 No error

FIG. 8b illustrates a chart of operational cycle versus expected lifebased on the data table of FIG. 8a as utilized by the expert systemaccording to an embodiment of the present invention. FIG. 8c illustratesa chart of failure probability based on the data of FIGS. 8a and 8baccording to an embodiment of the present invention. FIG. 8d illustratesa chart of the expected risk score as determined by the expert systemwith the chart showing probability of particular failure mode (e.g.stuck mode) along the x-axis for a keyboard and the sub-component levelof expected risk score along the y-axis according to an embodiment ofthe present invention.

FIG. 9a illustrates a data table for different sub-components utilizedto calculate an ecosystem level expect risk score including variables,operational cycles and expected life as utilized by the expert systemaccording to an embodiment of the present invention. FIG. 9b illustratesa chart of operational cycle versus expected life based on the datatable of FIG. 9a as utilized by the expert system according to anembodiment of the present invention. FIG. 9c illustrates a chart offailure probability based on the data of FIGS. 9a and 9b according to anembodiment of the present invention. FIG. 9d illustrates a chart of thecomponent expected risk score 901, system expected risk score 902 andecosystem expected risk score 903 as determined by the expert systemaccording to an embodiment of the present invention.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

In embodiments, the computer or computer system may be or include aspecial-purpose computer or machine that comprises specialized,non-generic hardware and circuitry (i.e., specialized discretenon-generic analog, digital, and logic based circuitry) for(independently or in combination) particularized for executing onlymethods of the present invention. The specialized discrete non-genericanalog, digital, and logic based circuitry may include proprietaryspecially designed components (e.g., a specialized integrated circuit,such as for example an Application Specific Integrated Circuit (ASIC),designed for only implementing methods of the present invention).

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or flash memory), a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain, or store a program for use byor in connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the C programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thepresent invention. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunction/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the function/acts specified in the flowchart and/or blockdiagram block or blocks.

It is understood in advance that although this disclosure includes adetailed description on conventional networks and cloud computingnetworks, implementation of the teachings recited herein are not limitedto any particular computing environment. Rather, embodiments of thepresent invention are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization and may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations) and may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

FIG. 10 depicts a cloud computing node according to an embodiment of thepresent invention. Cloud computing node 10 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 10 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 10, computer system/server 12 in cloud computing node10 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12 and may include both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention. Referring now to FIG. 11, cloud computingenvironment 50 comprises one or more cloud computing nodes 10 with whichlocal computing devices used by cloud consumers, such as, for example,personal digital assistant (PDA) or cellular telephone 54A, desktopcomputer 54B, laptop computer 54C, and/or automobile computer system 54Nmay communicate. Nodes 10 may communicate with one another and may begrouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof. This allows cloud computingenvironment 50 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device. It is understood that the types ofcomputing devices 54A-N shown in FIG. 11 are intended to be illustrativeonly and that computing nodes 10 and cloud computing environment 50 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention. Referring now to FIG. 12, a set of functionalabstraction layers provided by cloud computing environment 50 (FIG. 11)is shown. It should be understood in advance that the components,layers, and functions shown in FIG. 12 are intended to be illustrativeonly and embodiments of the invention are not limited thereto. Asdepicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and the system 96 to receive data andcalculate the EOL factors discussed above according to the presentinvention.

The disclosure discusses a method to use machine learning techniques toestimate the end of life (EOL) of a multi-vendor system. The inventionidentifies several methods to estimate the Risk score at the asset leveland also at the ecosystem level

The invention discusses a machine learning techniques using multivariateregression and neural networks to estimate the risk score.

The invention discusses a method for computing a risk score at an assetlevel and at an ecosystem level. This invention can be used within anenterprise, in datacenters, and across hybrid clouds. The inventionprovides the ability to compute the end of life of an ecosystem.

First, the invention sources historical data of hardware and softwareand analyzes the data to give usage pattern, criticality, etc. This ispartial generic step, with additional modeling framework becausecurrently no known solutions provide analytics and the closed-formrelations that may be effectively employed.

Using a modeling framework, the invention extracts input parameters fromsub-system such as keyboard, server/system, battery, monitor. Theseinputs are number of flickers, key-board response time, battery chargingrate, charge hold time over time, CPU/memory utilization, temperature,fan status, availability, Latency (ping round trip time), packet loss,frequency of incidents, mean time to repair, KPI threshold deviations,number of anomalies, events/log data, business criticality. Design data,manufacturer, benchmark, target. An input matrix is created from theseinputs. A state matrix is created for each of systems mentioned above.Correlation and criticality of input matrix & state matrix is createdwith outputs of each component, server/system.

The invention further applies PCA to create dominant features andregenerating input and state matrices.

Output from analytic stage is input into a “System Odometer” whichprovides remaining useful usage and in sync with current interventions,workload, and response time.

Output from the system odometer is provided as input to “Expert System”for computing probability of system survival for next N years, parts tobe replaced for system to survive next N years, and estimated life.

Output from the expert system is provided as input to ‘Capex/OpexOptimizer’ which computes below to give the final output which can beused by a system administrator to take further action.

All of these analytics engines provide final engine utilizing acognitive and artificial intelligence (AI) based system and providesfollowing: forecast of issues for next X days, trend of resourcecontention, sequence of processes to avoid contention, maintenancerecommendations, and optimal maintenance schedule.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers or ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method to manage economics and operationaldynamics of various information technology (IT) systems, the methodcomprising: collecting, by a computer, first data indicative ofoperation of a plurality of hardware components; collecting, by saidcomputer, second data indicative of operation of a plurality of softwarecomponents; evaluating, by said computer, said first and second datausing fuzzy logic to determine dynamic limits of various systemconditions indicative of operating performance of said plurality of saidhardware and software components; applying, by said computer, saidvarious system conditions to a multiple input, multiple output model ofanalysis to determine a residual useful life value for said plurality ofhardware and software components; performing, by the computer, aclosed-loop analysis in the form of a probability density function (PDF)for real-time data related to said plurality of said hardware andsoftware components to determine a number of times probability offailure has exceeded a predetermined threshold; creating, by thecomputer, a first qualitative value representing a hardware status ofthe plurality of said hardware components and a second qualitative valuerepresenting a software status of the plurality of said softwarecomponents; displaying, by the computer, said first and secondqualitative values in graphical form for evaluation by a systemoperator; and computing, by the computer, a probability of lifeexpectancy for said plurality of said hardware components and saidplurality of said software components based on said first and secondqualitative values and utilizing cognitive and artificial intelligencebased calculations to determine said probability.
 2. The method of claim1, further comprising: delivering data associated with said performingas a PDF value to an expert system for computing a probability offailure of at least one of said hardware and software components over afixed period of time.
 3. The method of claim 2, further comprising: anexpert report generating an output in a form of a risk score for saidplurality of said hardware and software components.
 4. The method ofclaim 3, wherein said risk score is indicative of an intensity of afailure probability.
 5. The method claim 4, further comprisingcalculating said risk score based on a formula represented by anindividual Asset Risk Score_(a1) equal toβ_(0a1)+X_(1a1)+β_(2a1)X_(2a1)+ . . . +β_(na1)X_(na1), where β_(0a1),β_(1a1) etc. are coefficients determined by regression techniques andX_(1a1), X_(2a1) etc. are variables and a cumulative risk scorerepresented by Cumulative Asset Risk Score=θ₀+θ₁Risk Score_(a1)+θ₂RiskScore_(a2)+ . . . +θ_(n)Risk Score_(an), where θ₀, θ₁ etc. arecoefficients determined by regression techniques and Risk Score_(a1),Risk Score_(a2) etc. are individual risk scores for respective assets.6. The method of claim 1, further comprising: collecting said first andsecond data from first sensors arranged physically on said hardwarecomponents and second sensors detecting operation of said softwarecomponents.
 7. The method of claim 1, further comprising: applying, inaddition to said fuzzy logic, a principal component analysis (PCA) tocreate dominant features and regenerating input and state matrices,wherein said PCA uses an orthogonal transformation to convert a set ofobservations of possibly correlated variables into a set of values oflinearly uncorrelated variables.
 8. The method of claim 1, furthercomprising: determining said probability of life expectancy to include aforecast of issues for a next X days, a trend of resource contentions, asequence of processes to avoid contention, maintenance recommendations,optimal maintenance schedule, and economic optimization.
 9. A computerprogram product comprising: a computer-readable storage device; and acomputer-readable program code stored in the computer-readable storagedevice, the computer readable program code containing instructionsexecutable by a processor of a computer system to implement a method tomanage economics and operational dynamics of various informationtechnology (IT) systems, the method comprising: collecting first dataindicative of operation of a plurality of hardware components;collecting second data indicative of operation of a plurality ofsoftware components; evaluating said first and second data using fuzzylogic to determine dynamic limits of various system conditionsindicative of operating performance of said plurality of said hardwareand software components; applying said various system conditions to amultiple input, multiple output model of analysis to determine aresidual useful life value for said plurality of hardware and softwarecomponents; performing a closed-loop analysis in the form of aprobability density function (PDF) for real-time data related to saidplurality of said hardware and software components to determine a numberof times probability of failure has exceeded a predetermined threshold;creating a first qualitative value representing a hardware status of theplurality of said hardware components and a second qualitative valuerepresenting a software status of the plurality of said softwarecomponents; displaying said first and second qualitative values ingraphical form for evaluation by a system operator; and computing aprobability of life expectancy for said plurality of said hardwarecomponents and said plurality of said software components based on saidfirst and second qualitative values and utilizing cognitive andartificial intelligence based calculations to determine saidprobability.
 10. The computer program product of claim 9, furthercomprising: delivering data associated with said performing as a PDFvalue to an expert system for computing a probability of failure of atleast one of said hardware and software components over a fixed periodof time.
 11. The computer program product of claim 10, furthercomprising: an expert report generating an output in a form of a riskscore for said plurality of said hardware and software components. 12.The computer program product of claim 11, wherein said a risk score isindicative of an intensity of a failure probability.
 13. The computerprogram product of claim 12, further comprising: calculating said riskscore based on a formula represented by an individual Asset RiskScore_(a1) equal to β_(0a1)+X_(1a1)+β_(2a1)X_(2a1)+ . . .+β_(na1)X_(na1), where β_(0a1), β_(1a1) etc. are coefficients determinedby regression techniques and X_(1a1), X_(2a1) etc. are variables and acumulative risk score represented by Cumulative Asset RiskScore=θ₀+θ₁Risk Score_(a1)+θ₂Risk Score_(a2)+ . . . +θ_(n)RiskScore_(an), where θ₀, θ₁ etc. are coefficients determined by regressiontechniques and Risk Score_(a1), Risk Score_(a2) etc. are individual riskscores for respective assets.
 14. The computer program product of claim9, further comprising: collecting said first and second data from firstsensors arranged physically on said hardware components and secondsensors detecting operation of said software components.
 15. Thecomputer program product of claim 9, further comprising: applying, inaddition to said fuzzy logic, a principal component analysis (PCA) tocreate dominant features and regenerating input and state matrices,wherein said PCA uses an orthogonal transformation to convert a set ofobservations of possibly correlated variables into a set of values oflinearly uncorrelated variables.
 16. A computer system, comprising: aprocessor; a memory coupled to said processor; and a computer readablestorage device coupled to the processor, the storage device containinginstructions executable by the processor via the memory to implement amethod to manage economics and operational dynamics of variousinformation technology (IT) systems, the method comprising: collectingfirst data indicative of operation of a plurality of hardwarecomponents; collecting second data indicative of operation of aplurality of software components; evaluating said first and second datausing fuzzy logic to determine dynamic limits of various systemconditions indicative of operating performance of said plurality of saidhardware and software components; applying said various systemconditions to a multiple input, multiple output model of analysis todetermine a residual useful life value for said plurality of hardwareand software components; performing a closed-loop analysis in the formof a probability density function (PDF) for real-time data related tosaid plurality of said hardware and software components to determine anumber of times probability of failure has exceeded a predeterminedthreshold; creating a first qualitative value representing a hardwarestatus of the plurality of said hardware components and a secondqualitative value representing a software status of the plurality ofsaid software components; displaying said first and second qualitativevalues in graphical form for evaluation by a system operator; andcomputing a probability of life expectancy for said plurality of saidhardware components and said plurality of said software components basedon said first and second qualitative values and utilizing cognitive andartificial intelligence based calculations to determine saidprobability.
 17. The method of claim 16 further comprising: deliveringdata associated with said performing as a PDF value to an expert systemfor computing a probability of failure of at least one of said hardwareand software components over a fixed period of time.
 18. The method ofclaim 17, further comprising: an expert report generating an output in aform of a risk score for said plurality of said hardware and softwarecomponents.
 19. The method of claim 18, wherein said risk score isindicative of an intensity of a failure probability.
 20. The methodclaim 19, further comprising: calculating said risk score based on theformula represented by an individual Asset Risk Score_(a1) equal toβ_(0a1)+β_(1a1)X_(1a1)+β_(2a1)X_(2a1)+ . . . +β_(na1)X_(na1), whereβ_(0a1), β_(1a1) etc. are coefficients determined by regressiontechniques and X_(1a1), X_(2a1) etc. are variables and a cumulative riskscore represented by Cumulative Asset Risk Score=θ₀+θ₁RiskScore_(a1)+θ₂Risk Score_(a2)+ . . . +θ_(n)Risk Score_(an), where θ₀, θ₁etc. are coefficients determined by regression techniques and RiskScore_(a1), Risk Score_(a2) etc. are individual risk scores forrespective assets.